It's an overdone Reddit-esque comment that adds nothing to the discussion.

This writeup legit coincidentally matches the asking-agents-to-make-code-faster-but-with-constraints-to-stop-agents-from-breaking-things writeup I posted on Monday: https://news.ycombinator.com/item?id=49803085

Front-end UI optimization is slightly trickier than optimizing strict algorithms, but I found that prompts to the agents to build tooling to track visual regressions are more than sufficient. The main issue (at least with GPT models) is that you have to be very explicit about the use of padding/margins/negative space.

That said, for my front end projects from scratch, I'm staying away from front-end JS frameworks and seeing how far and fast I can get with just HTML/CSS/vanilla JS shenanigans now that agents can wield them effectively.

That's extremely untrue, look at thread about AI/models with showdead and you'll see tons of flagged low-effort comments (in both pro and anti-AI directions)

Flags having a stronger influence than upvoting is a necessary algorithmic check against genuinely bad behavior/brigading, not disagreement. In general it works as intended, which is the best you can do without something potentially worse.

> necessary algorithmic check

Are there any stats available on the weighting ratios such as flags to upvotes, likes and comments per post after flagging, average time to frontpage to flag?

(G summarises the following[0], but a definitive source would clear up what the necessary algorithmic checks are).

[0]: https://share.google/aimode/cs9cKsvGMNRHs8OG1

I wish I could find the email, but awhile ago one YC startup pitched me on a six-figure job role solely dedicated to posting/commenting on Hacker News about it. Apparently I did not respond "no, that's stupid."

if anyone reading this wants to effectively pay me six figures to do what I'm already doing on HN, please send an e-mail to the address in my profile

Me too please

Yes, adding new features without regression is easy.

It's worth it if a) you have a decent sample size of data for your problem and b) you have a cost-effective infra to host it.

Notably the latter is more of the bottleneck, particularly with the price race-to-zero with models such as GPT-6 Luna.

There are allegedly multi-tenant LoRA offerings in the works which would change the hosting-pricing constraints considerably. Keeping my fingers crossed they materialize

From an editing perspective, this went mostly down to a) attributing the change to a model rather than the harness and b) vibes.

Yes, still 20% of input cost.

https://developers.openai.com/api/docs/pricing

That would only work if OpenAI were a monopoly, which they are not.